1 00:00:00,200 --> 00:00:03,440 Speaker 1: Now here's a highlight from Coast to Coast AM on 2 00:00:03,560 --> 00:00:06,840 Speaker 1: iHeart Radio, and welcome back to Coast to Coast. George 3 00:00:06,920 --> 00:00:09,920 Speaker 1: nor with you and welcome to open lines. Gina tells 4 00:00:09,960 --> 00:00:12,680 Speaker 1: me we have doctor Kevin from Indianapolis on our first 5 00:00:12,680 --> 00:00:16,120 Speaker 1: time caller line, and let's get him on right now. Kevin, 6 00:00:16,160 --> 00:00:20,479 Speaker 1: welcome to the show, doctor, Thanks for calling. Oh hi, George, 7 00:00:20,880 --> 00:00:23,720 Speaker 1: thank you very much for taking my call. Thank you. 8 00:00:23,800 --> 00:00:27,560 Speaker 1: I'm actually super nervous. I've listened to you for over 9 00:00:27,600 --> 00:00:29,760 Speaker 1: ten years as a coach insider. This is like the 10 00:00:29,800 --> 00:00:31,880 Speaker 1: first time I've ever stayed up late next to listen 11 00:00:31,920 --> 00:00:34,839 Speaker 1: to the show. Life. Well, I'm glad. What kind of 12 00:00:34,880 --> 00:00:37,959 Speaker 1: doctor are you? So, I'm an effect this disease specialist 13 00:00:38,000 --> 00:00:41,440 Speaker 1: and I essentially work as a nocturnis. I'm a critical 14 00:00:41,440 --> 00:00:44,239 Speaker 1: care doctor at night. And so what I was want 15 00:00:44,280 --> 00:00:46,760 Speaker 1: to call about is that, I mean, I am I 16 00:00:46,800 --> 00:00:49,199 Speaker 1: am the person who if you're sick coming to my 17 00:00:49,280 --> 00:00:52,080 Speaker 1: hospital and you need to be admitted on the one 18 00:00:52,159 --> 00:00:55,040 Speaker 1: doing that, Okay. And there are things that I'm just 19 00:00:55,080 --> 00:00:58,040 Speaker 1: hearing people talk about just in social media a little 20 00:00:58,040 --> 00:01:01,240 Speaker 1: bit on this show, in some other places. I just 21 00:01:01,360 --> 00:01:03,040 Speaker 1: I'm just hoping we just kind of talk for a minute, 22 00:01:03,040 --> 00:01:04,440 Speaker 1: and I just like to kind of get some things 23 00:01:04,440 --> 00:01:07,440 Speaker 1: out from my perspective. Sure, I love to hear it. 24 00:01:08,720 --> 00:01:11,000 Speaker 1: One thing that I know kind of gets brought up 25 00:01:11,080 --> 00:01:14,520 Speaker 1: is just this idea that COVID nineteen is really just 26 00:01:14,680 --> 00:01:17,080 Speaker 1: a bad flu. I assure you it is not just 27 00:01:17,120 --> 00:01:20,479 Speaker 1: a bad flu. I can probably count on my hand 28 00:01:20,880 --> 00:01:23,319 Speaker 1: the number of people I've seen in the nath last 29 00:01:23,400 --> 00:01:27,600 Speaker 1: number of years you have died directly from influenza. I'm seeing, 30 00:01:28,000 --> 00:01:30,760 Speaker 1: you know, people die every single day, and from this coronavirus. 31 00:01:30,760 --> 00:01:34,000 Speaker 1: I'm having to make very hard phone calls friends of family. 32 00:01:34,160 --> 00:01:37,839 Speaker 1: What are the ages of those people? Doctor? Generally? Yeah? 33 00:01:37,880 --> 00:01:40,080 Speaker 1: So um, actually, so I'm in Indiana. You can look 34 00:01:40,160 --> 00:01:42,680 Speaker 1: up the Indiana State Department of Health their data and 35 00:01:42,720 --> 00:01:45,360 Speaker 1: you can look right there that the death the people 36 00:01:45,440 --> 00:01:47,640 Speaker 1: who are dying from this, about ninety percent of them 37 00:01:47,640 --> 00:01:50,080 Speaker 1: are age sixty and higher. But there's also a different 38 00:01:50,080 --> 00:01:51,480 Speaker 1: graph on that you can look at the number of 39 00:01:51,520 --> 00:01:55,080 Speaker 1: people with positive cases and there's basically a bell curve 40 00:01:55,160 --> 00:01:57,680 Speaker 1: around the age of fifty and so I'm admitting a 41 00:01:57,680 --> 00:01:59,640 Speaker 1: lot of people who are in my age. I'm in 42 00:01:59,640 --> 00:02:04,880 Speaker 1: my full these thirties forties, fifties. But yeah, the people 43 00:02:04,880 --> 00:02:06,880 Speaker 1: who are dying so far are the older people. But 44 00:02:07,160 --> 00:02:10,800 Speaker 1: don't let that kind of really hide something though that's happening. 45 00:02:10,840 --> 00:02:12,760 Speaker 1: That if you don't work in a hospital, you don't know. 46 00:02:13,120 --> 00:02:15,960 Speaker 1: There's a lot of these younger people who are on ventilators. 47 00:02:16,000 --> 00:02:17,399 Speaker 1: I mean, they've been in a hospital for a week 48 00:02:17,480 --> 00:02:20,760 Speaker 1: or two. They're very, very sick. You know, they may survive, 49 00:02:21,360 --> 00:02:23,520 Speaker 1: but there's a very high morbidity with this as well. 50 00:02:23,760 --> 00:02:25,600 Speaker 1: And that's something else that I just don't think people 51 00:02:25,680 --> 00:02:27,799 Speaker 1: know if you're not really seeing what we're seeing. Another 52 00:02:27,800 --> 00:02:30,560 Speaker 1: thing compounding all this is that another thing they're really 53 00:02:30,600 --> 00:02:33,160 Speaker 1: hard to deal with is that we're not allowing any 54 00:02:33,200 --> 00:02:35,960 Speaker 1: visitors in the hospital. I bet you no, I don't 55 00:02:35,960 --> 00:02:38,560 Speaker 1: blame you. I don't blame you for that. And so 56 00:02:38,800 --> 00:02:41,040 Speaker 1: you know, friends and families, they're not really seen, you know, 57 00:02:41,240 --> 00:02:43,160 Speaker 1: they don't see their loved one actually going through you 58 00:02:43,160 --> 00:02:45,200 Speaker 1: know what I'm calling and telling them you know about 59 00:02:45,240 --> 00:02:46,840 Speaker 1: And I think that's another thing that kind of plays 60 00:02:46,880 --> 00:02:51,360 Speaker 1: into the public's a little bit in this perception of 61 00:02:51,440 --> 00:02:54,920 Speaker 1: how serious this is. Do you think that do you 62 00:02:55,000 --> 00:02:57,480 Speaker 1: think doctor that this has been going on a little 63 00:02:57,520 --> 00:03:01,320 Speaker 1: longer than people have said because the Latin lily, because 64 00:03:01,360 --> 00:03:04,919 Speaker 1: I probably thought it was the flu. Yeah, yeah, yeah, 65 00:03:04,960 --> 00:03:07,360 Speaker 1: no doubt in my mind. Because of the fiasco with 66 00:03:07,440 --> 00:03:10,520 Speaker 1: a lack of testing. Um, I mean, we don't know. 67 00:03:10,600 --> 00:03:13,480 Speaker 1: We may never really know, you know, exactly the extent 68 00:03:13,560 --> 00:03:16,320 Speaker 1: of this back in January. I don't know, and I 69 00:03:16,360 --> 00:03:18,440 Speaker 1: wouldn't view someone who would know, you know, what the 70 00:03:18,520 --> 00:03:20,519 Speaker 1: prevalence of this would be way back in December in 71 00:03:20,560 --> 00:03:22,720 Speaker 1: the United States. But I mean, I just I just 72 00:03:22,919 --> 00:03:25,560 Speaker 1: know this was going around in January and definitely in February, 73 00:03:26,120 --> 00:03:28,000 Speaker 1: at least at least in the major cities where they've 74 00:03:28,000 --> 00:03:31,240 Speaker 1: been in tuning and travel. There were reports of in 75 00:03:32,480 --> 00:03:36,200 Speaker 1: Washington area, I believe there was a what's called a 76 00:03:36,280 --> 00:03:39,640 Speaker 1: fluid project that they were wanting to actually test samples 77 00:03:39,640 --> 00:03:42,680 Speaker 1: of patients who had flu like illnesses test them for 78 00:03:42,760 --> 00:03:45,560 Speaker 1: COVID nineteen. I've lost track of this story, but there 79 00:03:45,640 --> 00:03:47,800 Speaker 1: was a story about a month ago where you know, 80 00:03:47,840 --> 00:03:49,760 Speaker 1: there were people in the government telling them not to 81 00:03:49,800 --> 00:03:51,760 Speaker 1: do this test. I don't know why. There's actually something 82 00:03:51,760 --> 00:03:53,560 Speaker 1: you might want to look into. It's a really interesting story, 83 00:03:53,560 --> 00:03:57,160 Speaker 1: but yeah, that's weird. I think at some point there 84 00:03:57,200 --> 00:03:59,720 Speaker 1: will be samples that were collected and they're just sitting 85 00:03:59,760 --> 00:04:02,440 Speaker 1: in that. They could go back and do your prevalence 86 00:04:02,440 --> 00:04:04,560 Speaker 1: studies and try to get a little bit better idea, 87 00:04:04,680 --> 00:04:07,800 Speaker 1: But I don't know, honestly at this point. I mean, 88 00:04:07,880 --> 00:04:09,680 Speaker 1: it's just gets everywhere. You know. This is kind of 89 00:04:09,680 --> 00:04:11,680 Speaker 1: what's on my mind, I kind of to some of 90 00:04:11,760 --> 00:04:13,520 Speaker 1: you who don't care what happened a month or two ago. 91 00:04:13,600 --> 00:04:16,200 Speaker 1: But to answer that question, yeah, I mean I think 92 00:04:16,240 --> 00:04:18,400 Speaker 1: it's been going, you know, a bit more and a 93 00:04:18,400 --> 00:04:20,760 Speaker 1: little bit longer than we all realized since we weren't 94 00:04:20,760 --> 00:04:23,640 Speaker 1: able to test. Doctor, are you seeing more or less 95 00:04:23,720 --> 00:04:27,520 Speaker 1: patients coming to the hospital now? So that's actually another 96 00:04:27,520 --> 00:04:30,159 Speaker 1: good question that gives a little bit of this air 97 00:04:30,240 --> 00:04:38,280 Speaker 1: of misunderstanding. So our system, our network's statistician how to presentation. 98 00:04:38,320 --> 00:04:40,640 Speaker 1: They do the presentation every Sunday, kind of go over 99 00:04:40,720 --> 00:04:43,240 Speaker 1: some stats, some numbers if you were to look at 100 00:04:43,279 --> 00:04:45,039 Speaker 1: and I bet you a lot of other hospitals are 101 00:04:45,200 --> 00:04:47,560 Speaker 1: kind of doing the same thing we're doing in other 102 00:04:47,600 --> 00:04:51,479 Speaker 1: cities like ours. But we are going We're moving heaven 103 00:04:51,560 --> 00:04:53,719 Speaker 1: and earth to keep people from having to go to 104 00:04:53,760 --> 00:04:56,800 Speaker 1: the hospital. So if you just look at the numbers, 105 00:04:56,839 --> 00:04:59,800 Speaker 1: of patients that's in the hospital, it's roughly the same 106 00:05:00,000 --> 00:05:03,520 Speaker 1: if you compare today to this same time last year. Okay, 107 00:05:03,839 --> 00:05:05,520 Speaker 1: you're kind of looking from day to day over a week. 108 00:05:05,960 --> 00:05:09,640 Speaker 1: The thing is, though, that the number of patients we 109 00:05:09,680 --> 00:05:12,360 Speaker 1: have at a nice C level, The number of patients 110 00:05:12,360 --> 00:05:15,760 Speaker 1: we have ventilators is double usual. The number of patients 111 00:05:15,800 --> 00:05:18,239 Speaker 1: are in progressive care, so these are people on higher 112 00:05:18,320 --> 00:05:22,080 Speaker 1: level levels of oxygen and whatnot, double what we normally have. Um, 113 00:05:22,480 --> 00:05:25,800 Speaker 1: I've worked nights actually doing you know, cover all the 114 00:05:26,080 --> 00:05:28,360 Speaker 1: basically everybody's in the hospital, but you know, fix patients 115 00:05:28,360 --> 00:05:30,400 Speaker 1: and stuff, and you know, I've had to ask for 116 00:05:30,560 --> 00:05:33,280 Speaker 1: you know, second doctor helping out at I'll work at 117 00:05:33,400 --> 00:05:36,240 Speaker 1: one of smaller hospitals on network. Usually I can cover 118 00:05:36,279 --> 00:05:38,120 Speaker 1: all that by myself, but I need a second doctor 119 00:05:38,240 --> 00:05:40,800 Speaker 1: be able to cover because if we have one patient 120 00:05:40,839 --> 00:05:43,440 Speaker 1: crashing on one floor, that could be someone crashing on 121 00:05:43,480 --> 00:05:45,680 Speaker 1: another floor that used to be I'll be able to 122 00:05:45,680 --> 00:05:48,160 Speaker 1: cover that just one person. So yeah, if you look 123 00:05:48,200 --> 00:05:50,400 Speaker 1: at the parking lot, you don't see as many cars 124 00:05:50,440 --> 00:05:54,360 Speaker 1: because we don't have visitors. We have you know, you 125 00:05:54,440 --> 00:05:57,440 Speaker 1: have patients. Yeah, yeah, because we're you know, we have 126 00:05:57,520 --> 00:05:59,960 Speaker 1: the same number of patients, but the number of patients 127 00:06:00,120 --> 00:06:04,240 Speaker 1: or sticker is doubled than usual when somebody, when somebody dies, doctor, 128 00:06:04,400 --> 00:06:08,359 Speaker 1: what happens to them? What kills them? Yeah, so actually 129 00:06:08,400 --> 00:06:10,080 Speaker 1: that was something else I was hoping we talk about. 130 00:06:10,120 --> 00:06:14,760 Speaker 1: So something that I'm seeing passed around is some version 131 00:06:15,400 --> 00:06:18,600 Speaker 1: and correct me if if it's different than this. But 132 00:06:18,680 --> 00:06:21,080 Speaker 1: if this is how I'm interpreting this, some version of 133 00:06:21,160 --> 00:06:24,880 Speaker 1: doctors like faking death certific is or fudging them or 134 00:06:24,920 --> 00:06:27,640 Speaker 1: something to that nature. First of all, that's fraud. That 135 00:06:27,760 --> 00:06:30,960 Speaker 1: the crime for anybody to do that. For one. For two, 136 00:06:31,440 --> 00:06:35,039 Speaker 1: for Indiana, we just had a webcast from the Indian 137 00:06:35,080 --> 00:06:39,120 Speaker 1: start State Department of Health. I believe it was just Thursday. Um, 138 00:06:39,200 --> 00:06:41,160 Speaker 1: anybody can actually look at this. It's like public. But 139 00:06:41,760 --> 00:06:43,920 Speaker 1: they give some instructions in terms of how this a 140 00:06:43,960 --> 00:06:47,600 Speaker 1: lot off certificate. Um, there's a certain way you do this. 141 00:06:47,760 --> 00:06:52,360 Speaker 1: So for example, let's just say somebody has a COVID 142 00:06:52,440 --> 00:06:58,240 Speaker 1: nineteen pneumonia and as a consequence of that, they develop 143 00:06:58,279 --> 00:07:00,440 Speaker 1: a blood clot they go through lungs, which is something 144 00:07:00,480 --> 00:07:03,920 Speaker 1: we're finding is pretty darn common. And UM, let's just 145 00:07:03,960 --> 00:07:07,280 Speaker 1: say that you know the physician, like I've built up 146 00:07:07,320 --> 00:07:08,760 Speaker 1: a good ten of these us if it gets in 147 00:07:08,760 --> 00:07:11,920 Speaker 1: the couple last couple of weeks myself, Um, besides someone 148 00:07:12,600 --> 00:07:16,680 Speaker 1: that it looks like they most approximately died from the 149 00:07:16,760 --> 00:07:19,720 Speaker 1: pulmonary embosom or the ZPE. Yeah, I would list that 150 00:07:19,760 --> 00:07:22,840 Speaker 1: as the most approximate cause of death. But that's going 151 00:07:22,840 --> 00:07:25,200 Speaker 1: to to be due to something else. It's going to 152 00:07:25,240 --> 00:07:28,080 Speaker 1: be due to that COVID nineteen. I mean that didn't happen. 153 00:07:28,600 --> 00:07:30,520 Speaker 1: If it wasn't for that, that's still the cause of 154 00:07:30,520 --> 00:07:32,880 Speaker 1: death really is the given nineteen and it gets you know, 155 00:07:32,960 --> 00:07:35,040 Speaker 1: counted as one of these numbers. A heart attack or 156 00:07:35,080 --> 00:07:38,440 Speaker 1: something something happens. Yeah, I mean we're seeing I never seen. 157 00:07:38,760 --> 00:07:41,560 Speaker 1: Uh it's a little confusing because of the false negative 158 00:07:41,640 --> 00:07:44,480 Speaker 1: rates and some of these things like this. But I've 159 00:07:44,480 --> 00:07:46,760 Speaker 1: seen a couple of younger people that I wouldn't have 160 00:07:46,840 --> 00:07:49,560 Speaker 1: otherwise thought it would have a heart attack, and uh 161 00:07:50,040 --> 00:07:52,840 Speaker 1: kind of wondering is it was related. Unfortunately, their tests 162 00:07:52,840 --> 00:07:57,440 Speaker 1: for negatives that thrombodic diseases. So you're your body having 163 00:07:57,520 --> 00:08:00,240 Speaker 1: blood caughts, you know, whether it's uh, you know, into 164 00:08:00,240 --> 00:08:02,360 Speaker 1: your lungs or in your legs or you know, same 165 00:08:02,360 --> 00:08:05,160 Speaker 1: thing happens in your heart. Actually finding that's a pretty 166 00:08:05,280 --> 00:08:08,120 Speaker 1: common thing that's being caused by this infection that we 167 00:08:08,160 --> 00:08:10,280 Speaker 1: don't see. We don't see, We definitely don't see an influence. 168 00:08:10,600 --> 00:08:13,560 Speaker 1: How long How long, Kevin, do you think people can 169 00:08:13,680 --> 00:08:18,520 Speaker 1: last who are not working now and uh, you know, 170 00:08:18,720 --> 00:08:20,760 Speaker 1: need to get back to work. This is a this 171 00:08:20,840 --> 00:08:23,720 Speaker 1: is part of the horrible situation of this whole traged now, 172 00:08:24,400 --> 00:08:25,920 Speaker 1: you know. And when you were talking like a minute 173 00:08:25,920 --> 00:08:28,400 Speaker 1: ago and you're talking about kind of the loss of 174 00:08:28,400 --> 00:08:30,880 Speaker 1: like human interaction, you know, it actually got me thinking 175 00:08:30,920 --> 00:08:34,160 Speaker 1: because to some degree, I don't know if I've noticed 176 00:08:34,200 --> 00:08:36,439 Speaker 1: as so much because I'm still working in hospital and 177 00:08:36,480 --> 00:08:39,839 Speaker 1: around people, right, but I've been off this last few days. 178 00:08:39,880 --> 00:08:42,079 Speaker 1: And another thing you're talking about, it was like it 179 00:08:42,120 --> 00:08:43,960 Speaker 1: was an exciting thing for me and my daughter and 180 00:08:44,000 --> 00:08:46,920 Speaker 1: my wife to go to the grocery store. Yeah it is. 181 00:08:46,960 --> 00:08:51,160 Speaker 1: I look forward to doing it. Yeah. Yeah, And unlike you, 182 00:08:51,200 --> 00:08:52,880 Speaker 1: I do wear a mask, but you know a lot 183 00:08:52,920 --> 00:08:56,480 Speaker 1: of people don't. It's it's it's okay, Yeah, it's a 184 00:08:56,480 --> 00:08:59,240 Speaker 1: really good question. I'm just totally not the person be 185 00:08:59,320 --> 00:09:04,720 Speaker 1: qualified to all Right, Well, Kevin, you keep listening and 186 00:09:04,800 --> 00:09:07,079 Speaker 1: you keep checking in with us. From time to time, 187 00:09:07,559 --> 00:09:11,040 Speaker 1: especially with your expertise. I think this is going to 188 00:09:11,080 --> 00:09:12,880 Speaker 1: be around for at least a couple of weeks to 189 00:09:13,080 --> 00:09:17,000 Speaker 1: a month or two longer, So just to keep letting 190 00:09:17,080 --> 00:09:20,920 Speaker 1: us know how you feel and what's going on. It 191 00:09:21,120 --> 00:09:24,880 Speaker 1: is a truly a bizarre situation. My biggest gripe with 192 00:09:25,000 --> 00:09:29,679 Speaker 1: this story when it broke was that the daily stats 193 00:09:30,679 --> 00:09:36,000 Speaker 1: are confusing and creating hysteria. At no point did I 194 00:09:36,000 --> 00:09:40,840 Speaker 1: ever say this disease was not a disease. But you know, 195 00:09:40,880 --> 00:09:43,280 Speaker 1: when you say two people died in the beginning, three 196 00:09:43,360 --> 00:09:47,520 Speaker 1: people died, and you've ignored the seasonal flu and all 197 00:09:47,600 --> 00:09:50,560 Speaker 1: the deaths associated with that up to sixty thousand a 198 00:09:50,640 --> 00:09:53,880 Speaker 1: year from twenty five thousand to sixty thousand a year, 199 00:09:53,960 --> 00:09:58,680 Speaker 1: depending on the season, it scares the living daylights out 200 00:09:58,679 --> 00:10:01,360 Speaker 1: of people. And I still think they're doing that. I 201 00:10:01,440 --> 00:10:03,840 Speaker 1: called a reporter from the Post Dispatch here in Saint 202 00:10:03,920 --> 00:10:07,240 Speaker 1: Louis who wrote a story that there were one hundred 203 00:10:07,240 --> 00:10:10,760 Speaker 1: and fifty deaths in the state of Missouri from the 204 00:10:11,400 --> 00:10:13,800 Speaker 1: COVID nineteen and I said, but how many have died 205 00:10:13,920 --> 00:10:18,199 Speaker 1: right now from the seasonal flu? And she's went, oh, 206 00:10:18,240 --> 00:10:21,120 Speaker 1: and I think that's important to put all of that 207 00:10:21,280 --> 00:10:24,840 Speaker 1: in perspective. Listen to more Coast to Coast AM every 208 00:10:24,880 --> 00:10:28,080 Speaker 1: weeknight at one am Eastern, and go to Coast to 209 00:10:28,080 --> 00:10:29,840 Speaker 1: Coast am dot com for more